doi:  10.3878/j.issn.1006-9895.1811.18189
2018年冬季南京三次暴雪过程微物理特征分析

Analysis on Microphysical Characteristics of Three Blizzard Processes in Nanjing in the Winter of 2018
摘要点击 151  全文点击 105  投稿时间:2018-07-05  
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基金:  国家重点研发计划“重大自然灾害监测预警与防范”重点专项2018YFC1507905,国家自然科学基金项目41775134,江苏省研究生科研创新项目KYCX17-0884、KYCX18-1010
中文关键词:  暴雪过程  雪花谱  微物理参数  雪花速度谱  Z-I关系
英文关键词:  Blizzard processes  Snowflake spectrum  Microphysical parameters  Snowflake velocity spectrum  Z-I relation
                    
作者中文名作者英文名单位
李遥南京信息工程大学大气物理学院,南京210044
牛生杰南京信息工程大学大气物理学院,南京210044;南京工业大学安全科学与工程学院, 南京210009
吕晶晶南京信息工程大学大气物理学院,南京210044;中国气象局气溶胶与云降水重点开放实验室,南京210044
王静南京信息工程大学大气物理学院,南京210044;中国气象局气溶胶与云降水重点开放实验室,南京210044
王天舒南京信息工程大学大气物理学院,南京210044
黄钦南京信息工程大学大气物理学院,南京210044
王元南京信息工程大学大气物理学院,南京210044
引用:李遥,牛生杰,吕晶晶,王静,王天舒,黄钦,王元.2019.2018年冬季南京三次暴雪过程微物理特征分析[J].大气科学,43(5):1095-1108,doi:10.3878/j.issn.1006-9895.1811.18189.
Citation:.2019.Analysis on Microphysical Characteristics of Three Blizzard Processes in Nanjing in the Winter of 2018[J].Chinese Journal of Atmospheric Sciences (in Chinese),43(5):1095-1108,doi:10.3878/j.issn.1006-9895.1811.18189.
中文摘要:
      为深入研究南京降雪微物理特征及变化,利用第二代激光雨滴谱仪PARSIVEL2、自动气象站观测资料及MICAPS数据,对2018年冬季南京的四次罕见强降雪过程中雪花的微物理参量进行分析。结果表明:(1)雪花谱基本呈多峰分布,个例1降雪强度增大时有小雪花向大雪花的转化,而其余三次过程则有雪花数浓度的显著增大。温度的差异使个例1大雪花形成机制与其余个例不同,最终导致了降雪稳定阶段,雪强增大的机制不同。(2)使用Gamma分布和M-P分布分别对四次降雪的不同阶段进行了拟合,Gamma 分布在各阶段的拟合优度均高于M-P分布拟合,降雪终止阶段拟合优度低于起始阶段及降雪全过程的拟合。四次降雪过程降雪粒子谱的Gamma分布分别为N=107D-0.21exp(-0.54D)、N=136D-0.54exp(-0.60D)、N=256D0.38exp(-1.01D)、N=9.39×104D4exp(-7.81D),其中,N为降雪粒子数浓度、D为雪花直径。(3)个例1在3 mm左右速度谱存在两个峰值,分别贴近结霜曲线和未结霜曲线,说明该次降雪大雪花的形成存在结霜增长和结霜碰并两种机制。(4)综合个例1、2、3,给出南京地区稳定的层状云强降雪的Z-I关系为Z=1708I1.51
Abstract:
      In order to study microphysical characteristics and changes of snowfall in Nanjing, observations of the second-generation laser raindrop spectrometer PARSIVEL2 and automatic weather stations and MICAPS data are used to analyze microphysical parameters of snowflakes in Nanjing in the winter of 2018. The results are as follows. (1) It can be seen that there was a conversion from small snowflakes to large snowflakes when the snowfall intensity in Case 1 increased, while significant increases in the snowflake concentration occurred in the other three processes. The difference in temperature makes the snow formation mechanism in Case 1 different from that in the other three cases, which eventually led to a stable snowfall stage, and the mechanisms for snow intensity increases are different. (2) Gamma distribution and M-P distribution are used to fit different stages of the four snowfalls respectively; the analysis shows that the goodness of fit using the Gamma distribution is higher than that using the M-P distribution at all stages, and the goodness of fit of the snowfall termination stage is lower than that of the initial stage and the entire process of snowfall. The Gamma distributions of the snow particle spectra for the four snowfalls are: N=107D-0.21exp(-0.54D), N=136D-0.54exp(-0.60D), N=256D0.38exp(-1.01D), N=9.39×104D4exp (-7.81D) (N is snowfall particle number concentration and D is snowflake diameter). (3) There are two peaks in the velocity spectrum for Case 1 at about 3 mm, which are close to the frosted and unfrosted curves respectively, indicating that there are two forming mechanisms for the snowfall. (4) Considering Case 1, 2, and 3, the comprehensive Z-I relationship for strong snowfalls during the period of stable stage of the stratiform cloud system in Nanjing is Z=1708I1.51.
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